⚡️ Speed up function sorter by 32,451%

[codeflash-ai]

📄 32,451% (324.51x) speedup for sorter in codeflash/bubble_sort.py

⏱️ Runtime : 532 milliseconds → 1.63 milliseconds (best of 143 runs)

📝 Explanation and details

Here’s the most efficient rewrite of your program:

• Replace the manual insertion sort (O(n²)) with Python’s built-in .sort() (Timsort, O(n log n) worst, very fast in practice—and stable/in-place!).
• Preserve all outputs and the function signature.
• The internal sort is now optimal for all reasonable inputs, no unnecessary copies or allocations.

No other changes are necessary:

• .sort() modifies the list in place and does so optimally.
• All function prints and return values are preserved.

This is the fastest way to sort a list in Python given your requirements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 55 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import random
import string
import sys

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import sorter

# unit tests

# ------------------- BASIC TEST CASES -------------------

def test_empty_list():
    # Test sorting an empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_single_element():
    # Test sorting a list with one element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_sorted_list():
    # Test sorting an already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_random_integers():
    # Test sorting a list of random integers
    arr = [3, 1, 4, 1, 5, 9, 2, 6]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_duplicates():
    # Test sorting a list with duplicate elements
    arr = [2, 3, 2, 1, 3, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

# ------------------- EDGE TEST CASES -------------------

def test_all_equal_elements():
    # Test sorting a list where all elements are the same
    arr = [7] * 10
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_negative_numbers():
    # Test sorting a list with negative numbers
    arr = [-3, -1, -4, -2, 0, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_mixed_positive_negative():
    # Test sorting a list with both positive and negative numbers
    arr = [5, -10, 0, 3, -1, 2, -5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_and_small_numbers():
    # Test sorting a list with very large and very small numbers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_floats():
    # Test sorting a list of floating point numbers
    arr = [3.1, 2.4, -1.5, 0.0, 2.4]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_strings():
    # Test sorting a list of strings
    arr = ["banana", "apple", "cherry", "date"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_empty_strings():
    # Test sorting a list with empty strings
    arr = ["", "a", "abc", "", "b"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_unicode_strings():
    # Test sorting a list with unicode strings
    arr = ["α", "β", "a", "b", "Α", "Β"]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_list_of_lists():
    # Test sorting a list of lists (should sort by first element of each sublist)
    arr = [[2, 3], [1, 2], [2, 2], [1, 1]]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_list_of_tuples():
    # Test sorting a list of tuples
    arr = [(2, 3), (1, 2), (2, 2), (1, 1)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_stability():
    # Test sorting is stable: equal elements retain their original order
    class StableObj:
        def __init__(self, key, idx):
            self.key = key
            self.idx = idx
        def __lt__(self, other):
            return self.key < other.key
        def __eq__(self, other):
            return self.key == other.key and self.idx == other.idx
        def __repr__(self):
            return f"({self.key}, {self.idx})"
    arr = [StableObj(1, 0), StableObj(2, 1), StableObj(1, 2), StableObj(2, 3)]
    codeflash_output = sorter(arr.copy()); sorted_arr = codeflash_output
    expected = [arr[0], arr[2], arr[1], arr[3]]

def test_non_comparable_types():
    # Test sorting a list with non-comparable types (should raise TypeError)
    arr = [1, "a", 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

def test_none_in_list():
    # Test sorting a list with None values (should raise TypeError in Python 3)
    arr = [1, None, 2]
    with pytest.raises(TypeError):
        sorter(arr.copy())

# ------------------- LARGE SCALE TEST CASES -------------------

def test_large_random_integers():
    # Test sorting a large list of random integers
    arr = random.sample(range(-100000, -99000), 1000)
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_duplicates():
    # Test sorting a large list with many duplicate elements
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_sorted():
    # Test sorting a large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_reverse_sorted():
    # Test sorting a large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_strings():
    # Test sorting a large list of random strings
    arr = [
        ''.join(random.choices(string.ascii_letters + string.digits, k=random.randint(1, 10)))
        for _ in range(1000)
    ]
    codeflash_output = sorter(arr.copy()); result = codeflash_output

def test_large_all_equal():
    # Test sorting a large list where all elements are the same
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random  # used for generating large random test cases
import sys  # used for edge case with sys.maxsize

# imports
import pytest  # used for our unit tests
from codeflash.bubble_sort import sorter

# unit tests

# ------------------------
# 1. BASIC TEST CASES
# ------------------------

def test_sorter_basic_sorted():
    # Already sorted list
    arr = [1, 2, 3, 4, 5]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.7μs -> 8.88μs (20.2% faster)

def test_sorter_basic_reverse():
    # Reverse sorted list
    arr = [5, 4, 3, 2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.79μs (22.8% faster)

def test_sorter_basic_unsorted():
    # Unsorted list with positive integers
    arr = [3, 1, 4, 5, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.54μs (23.4% faster)

def test_sorter_basic_duplicates():
    # List with duplicate elements
    arr = [2, 3, 2, 1, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.5μs -> 8.67μs (20.7% faster)

def test_sorter_basic_negatives():
    # List with negative numbers
    arr = [-1, -3, -2, 0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.83μs (19.8% faster)

def test_sorter_basic_mixed_signs():
    # List with both positive and negative numbers
    arr = [0, -10, 5, -2, 3]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.8μs -> 8.75μs (22.9% faster)

def test_sorter_basic_single_element():
    # List with a single element
    arr = [42]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.92μs -> 8.54μs (16.1% faster)

def test_sorter_basic_two_elements_sorted():
    # List with two elements already sorted
    arr = [1, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.96μs -> 8.75μs (2.39% faster)

def test_sorter_basic_two_elements_unsorted():
    # List with two elements, unsorted
    arr = [2, 1]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.92μs -> 8.46μs (17.2% faster)

# ------------------------
# 2. EDGE TEST CASES
# ------------------------

def test_sorter_edge_empty():
    # Empty list
    arr = []
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.71μs -> 8.25μs (5.56% faster)

def test_sorter_edge_all_equal():
    # All elements are the same
    arr = [7, 7, 7, 7, 7]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.08μs -> 8.29μs (9.56% faster)

def test_sorter_edge_large_values():
    # List with very large and very small integers
    arr = [sys.maxsize, -sys.maxsize-1, 0, 999999999, -999999999]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 9.08μs (25.7% faster)

def test_sorter_edge_floats():
    # List with floats
    arr = [3.1, 2.4, 5.6, 1.0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.4μs -> 9.96μs (14.2% faster)

def test_sorter_edge_mixed_int_float():
    # List with both ints and floats
    arr = [3, 2.5, 1, 4.0, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.6μs -> 9.83μs (18.2% faster)

def test_sorter_edge_with_zero():
    # List with zeros and negative/positive numbers
    arr = [0, -1, 1, 0, -2, 2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.6μs -> 8.75μs (20.9% faster)

def test_sorter_edge_alternating():
    # Alternating high/low values
    arr = [100, -100, 50, -50, 0]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.0μs -> 9.54μs (15.7% faster)

def test_sorter_edge_already_sorted_floats():
    # Already sorted floats
    arr = [-2.2, -1.1, 0.0, 1.1, 2.2]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 11.1μs -> 9.58μs (16.1% faster)

def test_sorter_edge_large_negative():
    # Large negative numbers
    arr = [-999999999, -888888888, -777777777]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.0μs -> 8.75μs (14.3% faster)

def test_sorter_edge_large_positive():
    # Large positive numbers
    arr = [999999999, 888888888, 777777777]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 9.25μs -> 8.79μs (5.22% faster)

def test_sorter_edge_minimal_difference():
    # Numbers with minimal difference
    arr = [1.000001, 1.000000, 1.000002]
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 10.9μs -> 9.29μs (17.5% faster)

# ------------------------
# 3. LARGE SCALE TEST CASES
# ------------------------

def test_sorter_large_sorted():
    # Large already sorted list
    arr = list(range(1000))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 80.5μs -> 35.6μs (126% faster)

def test_sorter_large_reverse():
    # Large reverse sorted list
    arr = list(range(999, -1, -1))
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 15.8ms -> 35.8μs (44183% faster)

def test_sorter_large_random():
    # Large random list of integers
    arr = random.sample(range(-10000, -9000), 1000)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.88ms -> 68.0μs (12964% faster)

def test_sorter_large_duplicates():
    # Large list with many duplicates
    arr = [random.choice([1, 2, 3, 4, 5]) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 6.55ms -> 57.7μs (11259% faster)

def test_sorter_large_floats():
    # Large list of floats
    arr = [random.uniform(-1000, 1000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.21ms -> 297μs (2664% faster)

def test_sorter_large_negative_positive():
    # Large list mixing negative and positive numbers
    arr = [random.randint(-10000, 10000) for _ in range(1000)]
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 8.38ms -> 71.7μs (11589% faster)

def test_sorter_large_alternating():
    # Large list alternating small and large numbers
    arr = []
    for i in range(500):
        arr.append(i)
        arr.append(1000 - i)
    expected = sorted(arr)
    codeflash_output = sorter(arr.copy()); result = codeflash_output

# ------------------------
# 4. ADDITIONAL EDGE CASES TO TEST FUNCTIONALITY
# ------------------------

def test_sorter_mutation_safety():
    # Ensure that sorter does not mutate the input list if not intended
    arr = [3, 2, 1]
    arr_copy = arr.copy()
    sorter(arr_copy)

def test_sorter_return_is_same_object():
    # Ensure that the returned list is the same object as input (since sorter sorts in-place)
    arr = [2, 1]
    codeflash_output = sorter(arr); result = codeflash_output # 10.2μs -> 8.79μs (15.6% faster)

def test_sorter_handles_large_identical_elements():
    # Large list with all identical elements
    arr = [42] * 1000
    codeflash_output = sorter(arr.copy()); result = codeflash_output # 81.2μs -> 34.2μs (138% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-sorter-mcfleytf and push.